Ab-initio computation of neutron-rich oxygen isotopes

We compute the binding energy of neutron-rich oxygen isotopes and employ the coupled-cluster method and chiral nucleon-nucleon interactions at next-to-next-to-next-to-leading order with two different cutoffs. We obtain rather well-converged results in model spaces consisting of up to 21 oscillator shells. For interactions with a momentum cutoff of 500 MeV, we find that 28O is stable with respect to 24O, while calculations with a momentum cutoff of 600 MeV result in a slightly unbound 28O. The theoretical error estimates due to the omission of the three-nucleon forces and the truncation of excitations beyond three-particle-three-hole clusters indicate that the stability of 28O cannot be ruled out from ab-initio calculations, and that three-nucleon forces and continuum effects play the dominant role in deciding this question.Comment: 5 pages + eps, 3 figure

A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes

A Spectral Stochastic Approach to the Inverse Heat Conduction Problem

A spectral stochastic approach to the inverse heat conduction problem (IHCP) is presented. In IHCP, one computes an unknown boundary heat flux from given temperature history data at a sensor location. In the stochastic inverse heat conduction problem (SIHCP), the full statistics of the boundary heat flux are computed given the stochastic nature of the temperature sensor data and in general accounting for uncertainty in the material data and process conditions. The governing continuum equations are solved using the spectral stochastic finite element method (SSFEM). The stochasticity of inputs is represented spectrally by employing orthogonal polynomials as the trial basis in the random space. Solution to the ill-posed SIHCP is then sought in an optimization sense in a function space that includes the random space. The gradient of the objective function is computed in a continuum sense using an adjoint framework. Finally, an example is presented in the solution of a one-dimensional stochastic inverse heat conduction problem in order to highlight the methodology and potential applications of the proposed techniques